land labour productivity (franck cachia, global strategy)
TRANSCRIPT
Measuring Labour and Land Productivity in Agriculture
Franck Cachia - Global Strategy to improve agricultural and rural statisticsGSARS - www.gsars.org
Expert Consultation – RuLIS(FAO, Rome, 7-8 November 2016)
1- Importance of labour productivity
Higher production/livelihoods
Higher wages and incomes
• SUSTAINABLE DEVELOPMENT GOALSo Indicator 2.3.1: “Volume of production per labour unit by classes of
farming/pastoral/forestry enterprise size”o Indicator 2.3.2: “Average income of small-scale food producers, by sex and
indigenous status”• MALABO DECLARATION: agricultural productivity x2 by 2025 in Africa
LABOUR IS THE MAIN INPUT IN MOST FARMS OF DEVELOPING COUNTRIES
Higher labour productivity generally
leads to
2- General definitions and questions
General definition“The number of units of output(s) produced per unit of labour used in agricultural production“
General formula
𝑙𝑙 =𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑙𝑙𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝑙𝑙 𝑂𝑂𝐴𝐴𝐴𝐴𝑢𝑢𝐴𝐴𝐴𝐴
𝐿𝐿𝐴𝐴𝐿𝐿𝐿𝐿𝐴𝐴𝐴𝐴 𝐴𝐴𝑖𝑖𝑢𝑢𝐴𝐴𝐴𝐴
Questions• Scope: what type of output/input should be considered• Measurement: how they should be quantified• Time: how to measure changes in labour productivity• Aggregation: producing farm-level/multi-output indicators
3- Different measurement concepts (1/2)
The measurement concept depends on the level of aggregation:
Productivity for: One commodity Several commodities
Output measured in: Physical quantitiesMonetary values
Commodity-equivalents
Labour input measured in:
Number of physical units (worker, days worked, etc.)
Examples:Tons of paddy rice produced per active worker in rice farms
Production value of cereals per active worker in cereal farms
Tons of cereals produced in wheat-equivalents per active worker in cereal farms
Productivity indicators should be quantity or volume-basedIf quantities are converted to values for aggregation, prices used should refer to a fixed period => constant-price measurements
If one commodity is considered, the change in productivity can be measured by comparing the simple indexes for the two time periods:
3- Different measurement concepts (2/2)
𝑙𝑙1 =𝑄𝑄1𝐿𝐿1
𝑙𝑙2 =𝑄𝑄2𝐿𝐿2
If several commodities are considered, the quantities can be aggregated by converting them to monetary units, using prices of a fixed reference period:
𝑙𝑙1 =∑𝑖𝑖=1𝑛𝑛 𝑢𝑢𝑖𝑖,1𝑄𝑄𝑖𝑖,1
𝐿𝐿1𝑙𝑙2 =
∑𝑖𝑖=1𝑛𝑛 𝑢𝑢𝑖𝑖,1𝑄𝑄𝑖𝑖,2𝐿𝐿2
Objective/context-specific
Objective of the productivity measure => What to include in the numerator and how to measure/quantify
4- Measuring agricultural output (numerator)
General guidelines
• Include all the farm outputs, including secondary/minor crops as well as output for minor season and any by-products (cf. Kelly et al. 1996 )
• Exclude output from non-agricultural activities• Include output from on-farm transformation/processing• If production values are needed, farm-gate prices should be
used (or at the first selling point)
5- Measuring labour input (denominator) (1/5)
Scope
• Labour involved in agricultural activities or on-farm processing
• All types of workers: permanent, casual/seasonal, family, exchange, paid/non-paid, etc.
• Workers under the minimum legal working age? Important contribution in seasonal tasks such as harvesting, planting, etc.
Measurement of labour input
• Several options
• With varying levels of complexity…
• And data collection requirements
5- Measuring labour input (denominator) (2/5)
Method 1 The number of workers active on the holding
Questions/issues/challenges: who is considered active ?• Threshold: one day per week ? Per month ?• Age limits• Inclusion of family labour
Limitations• It does not inform about the quantity of labour effectively used
on the farm (e.g. casual/seasonal/family labour)• It generally leads to an underestimation of labour productivity
Advantages• Straightforward, little data requirements
5- Measuring labour input (denominator) (3/5)
Method 2 The number of days worked on the holding
Questions/issues/challenges:• How many hours per day to be considered?• Reporting recall issues, especially for permanent/family labour
Limitations• It does not inform about the quantity of labour effectively used
on the farm (3 hours per day ≠ 6 hours per day)• It generally leads to an underestimation of labour productivity
Advantages• More precise than Method 1
5- Measuring labour input (denominator) (4/5)
Method 3 The number of hours actually worked on the holding
Questions/issues/challenges:• How many hours per day?• Unit not common for farmers, issue of piece-rate workers• Reporting recall issues
Limitations• Heavy data collection burden
Advantages• Gold Standard: Adequately measures the amount of labour
effectively used on the holding• More precise than Methods 1 and 2
5- Measuring labour input (denominator) (5/5)
Method 4 The number of full-time equivalents
Questions/issues/challenges:• What is a full-time equivalent (FTE) ?• FTE vary across countries• Issue of piece-rate workers• Reporting recall issuesLimitations• Heavy data collection burden• International comparisons may be biased because FTEs differAdvantages• Unit (FTE) more adapted to farmers’ practices• Adequately measures the amount of labour effectively used on
the holding if FTE are well-defined
6- Addressing differences in labour types (1/4)
Why providing data by labour types matters ?• Productivity of a skilled worker ≠ unskilled• Productivity of an adult ≠ child• Productivity of men ≠ women etc.
Why quality differences should be considered in productivity measurement ?• To assess the contribution of each category of worker• To explain differences in productivity (labour and total)• To measure total productivity: aggregation of inputs require the
use of input-specific weights/wages
6- Addressing differences in labour types (2/4)
Main criteria : • Age
• Sex
• Family labour vs. Hired labour
• Full-time vs. Part-time
• Permanent vs. Casual/Seasonal
• Educational levels
6- Addressing differences in labour types (4/4)
The result is a state-by-year panel dataset with :• Annual hours worked• Hourly compensation• By sex, age, education, and employment class
The dataset is used to:• Construct indexes for each State and the aggregate farm sector• Adjust indexes for quality change: labour hours having higher
marginal productivity are given higher weights in forming the index of labour input
Gold standard ?• High data collection burden
An example of classification matrix (USDA)
7- The productivity of labour depends on other inputs (2/2)
• Labour productivity = Partial productivity indicator
• Its contribution to total farm productivity depends on the use of other factors such as land, capital and intermediate inputs
• For example, improvements in labour productivity can be related to:
o Increased mechanization because machines often require less labour to cultivate a larger area
o Changes in farming practices: chemical vs. mechanical pest control etc.
• Implications: the difference in labour productivity between developed and developing countries is partially explained by the wider use of machinery in the former group
8- A few words on land productivity (1/2)
General definition“The number of units of output(s) produced per unit of land area“
General formula
𝑙𝑙 =𝐶𝐶𝐴𝐴𝐿𝐿𝑢𝑢 𝑂𝑂𝐴𝐴𝐴𝐴𝑢𝑢𝐴𝐴𝐴𝐴𝐿𝐿𝐴𝐴𝑖𝑖𝐿𝐿 𝐴𝐴𝐴𝐴𝐴𝐴𝐴𝐴
Questions• Scope:
o What area concept should be consideredo What output/crops
• Land quality characteristics
9- A few words on land productivity (2/2)
Land Area• Farm area -> Cultivated area -> Planted area -> Harvested area• Planted area -> Effective yield or land productivity• Cultivated area -> Land productivity of the cropping system (e.g.
rotations including fallow land)• Harvested area -> Closer to theoretical/biological yield
Land quality• Soil and land quality characteristics impact yields• Information on the main characteristics of the land/soils need to
be collected, to:o Understand/explain differences in yieldso Impute land values/costs (e.g. hedonic regressions)
• Elements attached to the land and contributing to increase its productivity such as irrigation, terracing, drainage, etc.) are considered as capital
• Jorgenson D. W, Ho, M.S., & Samuels, J. D. 2014. Long-term Estimates of U.S. Productivity and Growth, Prepared for Presentation at Third World KLEMS Conference, Growth and Stagnation in the World Economy, Tokyo, May 19-20, 2014
• Kelly, V., Hopkins, J., Reardon, T., & Crawford, E. 1996. Improving the Measurement and Analysis of African Agricultural Productivity Promoting Complementarities Between Micro and Macro Data. Technical Paper No. 27. Office of Sustainable Development. Bureau for Africa. USAID publication. Washington D.C.
• OECD. 2001b. Measuring Productivity, Measurement of Aggregate and Industry-level Productivity Growth. OECD Manual, Paris
• Shumway, C. R., Fraumeni, B. M., Fulginiti, L. E., Samuels, J. D., & StefanouS.E. 2015. Measurement of U.S. Agricultural Productivity: A 2014 Review of Current Statistics and Proposals for Change, Working Paper Series WP 2015-12, School of Economic Science, Washington State University
10 - References